Real-time emotion recognition for gaming using deep convolutional network features

Ouellet, Sébastien

doi:10.48550/arxiv.1408.3750

Cited by 3 publications

(1 citation statement)

References 12 publications

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“…As shown in [ 4 ], many studies have been conducted to recognise basic facial expressions of emotion using a wide variety of input features and classification methods. While static images provide enough information to decently perform such a task [ 5 ], more subtle facial expression cues require temporal information [ 6 ]. Several large datasets for FER are available both with static and dynamic information.…”

Section: Introductionmentioning

confidence: 99%

Facial Motion Analysis beyond Emotional Expressions

Porta-Lorenzo

Vázquez-Enríquez

Pérez-Pérez

et al. 2022

Sensors

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Facial motion analysis is a research field with many practical applications, and has been strongly developed in the last years. However, most effort has been focused on the recognition of basic facial expressions of emotion and neglects the analysis of facial motions related to non-verbal communication signals. This paper focuses on the classification of facial expressions that are of the utmost importance in sign languages (Grammatical Facial Expressions) but also present in expressive spoken language. We have collected a dataset of Spanish Sign Language sentences and extracted the intervals for three types of Grammatical Facial Expressions: negation, closed queries and open queries. A study of several deep learning models using different input features on the collected dataset (LSE_GFE) and an external dataset (BUHMAP) shows that GFEs can be learned reliably with Graph Convolutional Networks simply fed with face landmarks.

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Section: Introductionmentioning

confidence: 99%

Facial Motion Analysis beyond Emotional Expressions

Porta-Lorenzo

Vázquez-Enríquez

Pérez-Pérez

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

RETRACTED ARTICLE: Affective computing model for natural interaction based on large-scale self-built dataset

Wan

2023

SN Appl. Sci.

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Because of the development of metaverse, robotics, emotional computing has become a very active research topic. And the most important part of affective computing is facial expression recognition, The existing deep learning-based face expression recognition methods are trained on small face expression datasets, and the current datasets have the problems of low quantity, low quality and category imbalance, and the models are prone to overfitting when trained on these small datasets. Compared with other methods using small datasets, this paper proposes a method to build a large face expression dataset by collecting data from the Internet. It also uses both downsampling and data augmentation to solve the category imbalance problem. Meanwhile, this paper experiments the effect of different network structures on face expression recognition based on this brand new dataset. Compared to approaches using small datasets such as raf-db, fer2013, and relatively large but category-unbalanced datasets such as ExpW and AffectNet, the densenet-based model proposed in this paper, trained on this dataset, achieved 97.5% accuracy on the CK+ benchmark, achieve the state-of-the-art performance, which confirms the effectiveness of our proposed approach.

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Examining learners’ cognitive processes and learning emotions in digital game-based vocabulary learning: evidence from eye-tracking technology and facial emotion recognition

Wang,

Chen,

Hung

2024

ITSE

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Purpose This research explored the intersection of cognitive processes, emotions and their impacts on digital game-based vocabulary learning (DGVL) among university students. Recognizing the scant research in this area, especially with integrating innovative technologies, this study aims to understand the influence of these elements using advanced monitoring tools. Design/methodology/approach This inquiry was carried out as an observational study involving 44 university students segmented into three English language proficiency levels: high, intermediate and low based on their English course scores. The methodological tools included a portable eye tracker to observe visual behaviors and deep learning technology to identify and analyze the participants’ emotional responses and engagement with the DGVL during the learning process. Findings The results showed that distinct fixation sequences and variations in visual attention during DGVL were correlated with different levels of competency, suggesting a direct correlation between visual engagement and language competence. In addition, emotional transitions, predominantly from engagement (“flow”) to challenge (“frustration”), were common among participants, reflecting the emotional dynamics of learning. Furthermore, all participants consistently focused on the English vocabulary definitions, indicative of their targeted approach to understanding and test preparation. These findings highlighted the intricate dynamics between emotions and cognitive processes in learning environments. Originality/value Contribution of this study shows the interplay of cognitive engagement and emotional experiences in the context of DGVL. It underscored the complex nature of these factors and their collective influence on learners’ visual and emotional engagement, offering valuable implications for educational strategies and technological applications in language learning.

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Real-time emotion recognition for gaming using deep convolutional network features

Cited by 3 publications

References 12 publications

Facial Motion Analysis beyond Emotional Expressions

Facial Motion Analysis beyond Emotional Expressions

RETRACTED ARTICLE: Affective computing model for natural interaction based on large-scale self-built dataset

Examining learners’ cognitive processes and learning emotions in digital game-based vocabulary learning: evidence from eye-tracking technology and facial emotion recognition

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